The DTI-GAT contains the feature (PsePSSM, Fingerprint) encoder, the GAT embedding module and a final interaction decoder. Feature encoder is implemented as two MLP layers for protein and drug respectively. The input dimensions are 220 (PsePSSM) and 881 (fingerprint) and the output dimensions are both 256. The GAT embedding module consists of a Multi-head GAT layer, based on PyTorch geometric [1]}. The hidden dimension and output dimension both are set to 256 for all datasets. In building the adjacency matrix, the setting of the common neighbor threshold is different in different datasets, it is set to 1 in Nuclear Receptor, 3 in GPCR, Ion Channel and Enzyme dataset. The final interaction decoder includes three MLP layers, which input dimension \(2 \times 256 = 512\)  and the hidden dimension is also 256. The output is a scalar, then be computed by a sigmoid function to get the final interaction probability.
